Joseph Howse - Learning OpenCV 5 Computer Vision with Python - Fourth Edition

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Early Access Publication: Learning OpenCV 5 Computer Vision with Python

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Welcome to Packt Early Access. Were giving you an exclusive preview of this book before it goes on sale. It can take many months to write a book, but our authors have cutting-edge information to share with you today. Early Access gives you an insight into the latest developments by making chapter drafts available. The chapters may be a little rough around the edges right now, but our authors will update them over time. You can dip in and out ofthis bookorfollow alongfrom start to finish; Early Access is designed to be flexible. We hope you enjoy getting to know more about the process of writing a Packt book.

1. Chapter 1: Setting Up OpenCV
2. Chapter 2: Handling Files, Cameras, and GUIs
3. Chapter 3: Processing Images with OpenCV
4. Chapter 4: Depth Estimation and Segmentation
5. Chapter 5: Detecting and Recognizing Faces
6. Chapter 6: Retrieving Images and Searching Using Image Descriptors
7. Chapter 7: Building Custom Object Detector
8. Chapter 8: Tracking Objects
9. Chapter 9: Camera Models and Augmented Reality
10. Chapter 10: 3D Reconstruction and Navigation
11. Chapter 11: NeuraNetworks with OpenCV - an Introduction
12. Chapter 12: OpenCV Applications at Scale

Computer vision makes many futuristic-sounding tasks a reality. Two such tasks are face detection (locating faces in an image) and face recognition (identifying a face as belonging to a specific person). OpenCV implements several algorithms for face detection and recognition. These have applications in all sorts of real-world contexts, from security to entertainment.This chapter introduces some of OpenCV's face detection and recognition functionality, along with data files that define particular types of trackable objects. Specifically, in this chapter, well look at Haar cascade classifiers, which analyze the contrast between adjacent image regions to determine whether or not a given image or sub-image matches a known type. We consider how to combine multiple Haar cascade classifiers in a hierarchy so that one classifier identifies a parent region (for our purposes, a face) and other classifiers identify child regions (such as eyes).We also take a detour into the humble but important subject of rectangles. By drawing, copying, and resizing rectangular image regions, we can perform simple manipulations on image regions that we are tracking.We will cover the following topics:

• Understanding Haar cascades
• Finding the pre-trained Haar cascades that come with OpenCV. These include several face detectors
• Using Haar cascades to detect faces in still images and videos
• Gathering images to train and test a face recognizer
• Using several different face recognition algorithms: Eigenfaces, Fisherfaces, and Local Binary Pattern Histograms (LBPHs)
• Copying rectangular regions from one image to another, with or without a mask
• Using a depth camera to distinguish between a face and the background based on depth
• Swapping two people's faces in an interactive application

While this chapter focuses on classic approaches to face detection and recognition, we will go on to explore advanced new models by using OpenCV with a variety of other libraries in Chapter 11, Neutral Networks with OpenCV an Introduction.

By the end of this chapter, we will have integrated face tracking and rectangle manipulations into Cameo, the interactive application that we have developed in previous chapters. Finally, we will have some face-to-face interaction!

This chapter uses Python, OpenCV, and NumPy. As part of OpenCV, it uses the optional opencv_contrib modules, which include functionality for face recognition. Some parts of this chapter use OpenCV's optional support for OpenNI 2 to capture images from depth cameras. Please refer back to Chapter 1, Setting Up OpenCV, for installation instructions.The complete code for this chapter can be found in this book's GitHub repository, https://github.com/PacktPublishing/Learning-OpenCV-5-Computer-Vision-with-Python-Fourth-Edition, in the chapter05 folder. Sample images are in the repository images folder.A subset of the chapters sample code can be edited and run interactively in Google Colab at https://colab.research.google.com/github/PacktPublishing/Learning-OpenCV-5-Computer-Vision-with-Python-Fourth-Edition/blob/main/chapter05/chapter05.ipynb.

When we talk about classifying objects and tracking their location, what exactly are we hoping to pinpoint? What constitutes a recognizable part of an object?Photographic images, even from a webcam, may contain a lot of detail for our (human) viewing pleasure. However, image detail tends to be unstable with respect to variations in lighting, viewing angle, viewing distance, camera shake, and digital noise. Moreover, even real differences in physical detail might not interest us for classification. Joseph Howse, one of this book's authors, was taught in school that no two snowflakes look alike under a microscope. Fortunately, as a Canadian child, he had already learned how to recognize snowflakes without a microscope, as the similarities are more obvious in bulk.Hence, having some means of abstracting image detail is useful in producing stable classification and tracking results. The abstractions are called features, which are said to be extracted from the image data. There should be far fewer features than pixels, though any pixel might influence multiple features. A set of features is represented as a vector (conceptually, a set of coordinates in a multidimensional space), and the level of similarity between two images can be evaluated based on some measure of the distance between the images' corresponding feature vectors.

Later, in Chapter 6, Retrieving Images and Searching Using Image Descriptors